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Prévoir sans persistance

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The forecasting literature has identified three important and broad issues: the predictive content is unstable over time, in-sample and out-of-sample discordant results and the problematic statistical inference with highly persistent predictors. In this paper, we simultaneously address these three issues, proposing to directly treat the persistence of forecasting variables before use. We thus cut-out the low frequency components and show, in simulations and on financial data, that this pre-treatment improves the predictive power of the studied economic variables

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  • Christophe Boucher & Bertrand Maillet, 2012. "Prévoir sans persistance," Documents de travail du Centre d'Economie de la Sorbonne 12001, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    • Handle: RePEc:mse:cesdoc:12001
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    More about this item

    Keywords

    Forecasting; persistence; filters;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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